IVQ 1-50

1. What is LangFuse and what problem does it solve?

2. How do you log LLM requests in LangFuse?

3. What is a trace in LangFuse?

4. How does LangFuse handle prompt and response evaluation?

5. What is the role of langfuse.log() in the SDK?

6. How do you integrate LangFuse with LangChain or LlamaIndex?

7. What is an observation in LangFuse?

8. How does LangFuse support multi-step traces in agent workflows?

9. Can you use LangFuse in both synchronous and asynchronous Python code?

10. How are traces visualized in the LangFuse dashboard?

11. What metadata can be attached to traces in LangFuse?

12. How do you group and filter traces in LangFuse?

13. What are score evaluations in LangFuse and how are they used?

14. How can LangFuse help with debugging hallucinations in LLM outputs?

15. Is LangFuse suitable for production usage and scale?

16. What are the main components of a LangFuse trace (e.g., span, observation, score)?

17. How do you manually create a trace in LangFuse using the SDK?

18. What is a span in LangFuse and how does it relate to a trace?

19. How does LangFuse differ from OpenTelemetry or similar logging tools?

20. Can you export logs or traces from LangFuse for external analysis?

21. How does LangFuse support prompt versioning and comparisons?

22. What is the role of LangfuseSpan and how do you use it?

23. How can LangFuse be used to monitor tool usage inside LLM agents?

24. Does LangFuse support API key-based authentication for the SDK?

25. How do you set custom properties on a trace in LangFuse?

26. What is the recommended way to log retries or fallbacks in LangFuse?

27. How does LangFuse visualize nested chains or complex workflows?

28. Can LangFuse be self-hosted? What are the trade-offs?

29. How does LangFuse handle rate limits or high-frequency trace logging?

30. What kind of alerting or anomaly detection does LangFuse support (if any)?

31. How do you integrate LangFuse with a FastAPI or Flask backend?

32. Can LangFuse be used to track evaluation metrics like accuracy or latency?

33. What built-in visualizations does LangFuse offer for evaluating model quality?

34. How do you organize and label traces for different environments (dev, prod)?

35. What kinds of inputs and outputs can be logged with LangFuse?

36. How does LangFuse handle long-running or streaming tasks?

37. Can you search and filter traces based on custom metadata in LangFuse?

38. Is LangFuse compatible with OpenAI, Anthropic, and other LLM APIs?

39. How do you monitor multiple agents or chains in a single trace?

40. What’s the difference between a score and a tag in LangFuse?

41. How do you store user feedback or ratings in LangFuse?

42. Can LangFuse help you AB test multiple prompts or models?

43. How are parent-child relationships between spans handled in LangFuse?

44. How do you redact sensitive data before logging it in LangFuse?

45. How do you use LangFuse’s SDK in a Jupyter Notebook?

46. Does LangFuse support TypeScript or JavaScript SDKs?

47. What is the performance overhead of using LangFuse in an app?

48. How do you track tool usage in a ReAct or function-calling agent with LangFuse?

49. Can you add screenshots or file attachments to traces in LangFuse?

50. What role does LangFuse play in evaluating RAG (Retrieval-Augmented Generation) systems?